On fractionally-supervised classification with nominated samples
Bibliographic record
Abstract
Fractionally-supervised classification (FSC) is a recently proposed classification method in the literature that combines the finite mixture model (FMM), weighted likelihood, and Expectation-Maximization (EM) algorithm to adjust the weight of labeled (unlabeled) data in the training process of a classifier and obtain the best classification result. All the results in the literature pertinent to FSC are based on simple random sampling (SRS). In this thesis, we extend FSC approach to a ranked-based type sampling design called nominated sampling (NS), which collects more representative data than SRS from tails of the underlying population. We show that the usual EM algorithm for finite mixture modeling using nominated samples leads to incorrect maximization problems. In this thesis, we propose a set of proper latent variables and modify the usual EM algorithm for the FSC approach based on maxima (minima) nominated samples and evaluate the estimation and classification results. We compare the mean squared error (MSE) of estimates obtained by FSC with two EM algorithms and observe that the EM algorithm with proper latent variable has a higher relative efficiency when applying NS samples. Moreover, we compute the adjusted Rand index (ARI) to assess the classification performance in different weights of unlabeled data and determine the best choice of weight for the purpose of FSC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".